. This proposal describes a five year career development plan for Soumya Raychaudhuri in statistical genetics. Dr. Raychaudhuri is a Rheumatology fellow at the Brigham and Women's Hospital (BWH). He will integrate his background in bioinformatics with the statistical genetics resources of the Broad Institute and the immunological and clinical strengths of BWH. Dr. Raychaudhuri will be mentored by Mark Daly, an associate and director for the Computational Biology lab of the Medical and Population Genetics Program at Broad;Dr. Daly is a recognized expert in the statistical genetics of auto-immune disease with a strong mentoring track record. Dr. Raychaudhuri will work closely with David Altshuler, Peter Gregerson, Dan Solomon, and Robert Plenge, and receive from them general career advice and specific scientific guidance on the completion of the proposed project. He will work with Lars Klareskog and Paul de Bakker in collaboration to help genotype and analyze cohorts of rheumatoid arthritis (RA) patients. The research program will emphasize the genetics of RA, with a goal of identifying and replicating new loci that confer increased disease risk. Detection of RA genes has been difficult since it is a polygenic disease that probably involves modest effects from many genes with complex interactions. Genetic studies have thus far identified PTPN22, TNFAIP3, and STAT4 as replicable loci outside the Major Histocompatibility Complex. We hypothesize that some susceptibility alleles are common variants. Therefore, to identify unrecognized loci, we focus our efforts on the combined analysis of three genome-wide association scans (GWAS) in RA. Each individual study may be too small to detect necessary effects, however. We further hypothesize that involved genes may be in common pathways or share biological processes. Computational analysis of functional genomics data (gene expression, protein interaction, scientific text) may be able to elucidate relationships. Testing functionally related gene variants in concert for disease association may increase the power of genetics approaches. We propose: (1) developing and applying new methods to combine small RA WGAS to increase power, and (2) developing and applying novel bioinformatics approaches to integrate functional genomics data into statistical genetic analysis. Relevance. The research proposed here aspires to find disease genes in RA using bioinformatics approaches with population genetics data. The identification of these genes has implications for (1) the rapid identification and diagnosis of RA, (2) recognizing critical pathways in the pathogenesis of the disease, and (3) defining future pharmaceutical targets.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Clinical Investigator Award (CIA) (K08)
Project #
5K08AR055688-05
Application #
8321101
Study Section
Arthritis and Musculoskeletal and Skin Diseases Special Grants Review Committee (AMS)
Program Officer
Wang, Yan Z
Project Start
2008-09-15
Project End
2013-07-31
Budget Start
2012-09-01
Budget End
2013-07-31
Support Year
5
Fiscal Year
2012
Total Cost
$123,458
Indirect Cost
$9,145
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Slowikowski, Kamil; Hu, Xinli; Raychaudhuri, Soumya (2014) SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics 30:2496-7
Han, Buhm; Diogo, Dorothée; Eyre, Steve et al. (2014) Fine mapping seronegative and seropositive rheumatoid arthritis to shared and distinct HLA alleles by adjusting for the effects of heterogeneity. Am J Hum Genet 94:522-32
Liao, Katherine P; Diogo, Dorothee; Cui, Jing et al. (2014) Association between low density lipoprotein and rheumatoid arthritis genetic factors with low density lipoprotein levels in rheumatoid arthritis and non-rheumatoid arthritis controls. Ann Rheum Dis 73:1170-5
Yu, Yi; Triebwasser, Michael P; Wong, Edwin K S et al. (2014) Whole-exome sequencing identifies rare, functional CFH variants in families with macular degeneration. Hum Mol Genet 23:5283-93
Trynka, Gosia; Sandor, Cynthia; Han, Buhm et al. (2013) Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet 45:124-30
Viatte, Sebastien; Plant, Darren; Raychaudhuri, Soumya (2013) Genetics and epigenetics of rheumatoid arthritis. Nat Rev Rheumatol 9:141-53
Kar, S P; Seldin, M F; Chen, W et al. (2013) Pathway-based analysis of primary biliary cirrhosis genome-wide association studies. Genes Immun 14:179-86
Seddon, Johanna M; Yu, Yi; Miller, Elizabeth C et al. (2013) Rare variants in CFI, C3 and C9 are associated with high risk of advanced age-related macular degeneration. Nat Genet 45:1366-70
Jia, Xiaoming; Han, Buhm; Onengut-Gumuscu, Suna et al. (2013) Imputing amino acid polymorphisms in human leukocyte antigens. PLoS One 8:e64683
Raychaudhuri, Soumya; Sandor, Cynthia; Stahl, Eli A et al. (2012) Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat Genet 44:291-6

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